OCD: Online convergence detection for evolutionary multi-objective algorithms based on statistical testing

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5467 LNCS, Issue , 2010, Pages 198-215

  Wagner, Tobias ^a   Trautmann, Heike ^a   Naujoks, Boris ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ACCURACY; COMPUTATIONAL DEMANDS; CONVERGENCE DETECTION; FITNESS EVALUATIONS; INDUSTRIAL OPTIMIZATION; MULTI OBJECTIVE ALGORITHM; PARETO FRONT; PERFORMANCE INDICATORS; REFERENCE POINTS; STAND -ALONE; STATISTICAL TESTING; TEST FUNCTIONS;

BENCHMARKING; EVOLUTIONARY ALGORITHMS; OPTIMIZATION; STATISTICAL TESTS;

CONVERGENCE OF NUMERICAL METHODS;

EID: 78650747733     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-01020-0_19     Document Type: Conference Paper

References (33)

1. Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo- Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 832-842. Springer, Heidelberg (2004)
2. Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research 181(3), 1653-1669 (2007) (Pubitemid 46498552)
3. Zitzler, E., Thiele, L., Bader, J.: SPAM: Set preference algorithm for multiobjective optimization. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 847-858. Springer, Heidelberg (2008)
4. Wagner, T., Beume, N., Naujoks, B.: Pareto-, aggregation-, and indicator-based methods in many-objective optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 742-756. Springer, Heidelberg (2007)
5. Emmerich, M., Giannakoglou, K., Naujoks, B.: Single- and multi-objective evolutionary optimization assisted by gaussian random field metamodels. IEEE Trans. on Evolutionary Computation 10(4), 421-439 (2006)
6. Knowles, J.: ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Trans. on Evolutionary Computation 10(1), 50-66 (2006)
7. Ponweiser, W., Wagner, T., Biermann, D., Vincze, M.: Multiobjective optimization on a limited amount of evaluations using model-assisted S-metric selection. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 784-794. Springer, Heidelberg (2008)
8. Deb, K., Mohan, M., Mishra, S.: A fast multi-objective evolutionary algorithm for finding well-spread pareto-optimal solutions. KanGAL report 2003002, Indian Institute of Technology, Kanpur, India (2003)
9. Huang, V.L., Qin, A.K., Deb, K., Zitzler, E., Suganthan, P.N., Liang, J.J., Preuss, M., Huband, S.: Problem definitions for performance assessment of multiobjective optimization algorithms. Technical report, Nanyang Technological University (2007)
10. Rudolph, G., Naujoks, B., Preuss, M.: Capabilities of EMOA to detect and preserve equivalent pareto subsets. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 36-50. Springer, Heidelberg (2007)
11. Kumar, A., Sharma, D., Deb, K.: A hybrid multi-objective optimization procedure using PCX based NSGA-II and sequential quadratic programming. In: Michalewicz, Z., Reynolds, R.G. (eds.) Congress on Evolutionary Computation (CEC). IEEE Press, Piscataway (2007)
12. Deb, K., Lele, S., Datta, R.: A hybrid evolutionary multi-objective and SQP based procedure for constrained optimization. In: Kang, L., Liu, Y., Zeng, S. (eds.) ISICA 2007. LNCS, vol. 4683, pp. 36-45. Springer, Heidelberg (2007)
13. Emmerich, M., Deutz, A., Beume, N.: Gradient-based/Evolutionary relay hybrid for computing pareto front approximations maximizing the S-metric. In: Bartz- Beielstein, T., Blesa Aguilera, M.J., Blum, C., Naujoks, B., Roli, A., Rudolph, G., Sampels, M. (eds.) HCI/ICCV 2007. LNCS, vol. 4771, pp. 140-156. Springer, Heidelberg (2007)
14. Deb, K., Jain, S.: Running performance metrics for evolutionary multi-objective optimization. In: Simulated Evolution and Learning (SEAL), pp. 13-20 (2002)
15. Rudenko, O., Schoenauer, M.: A steady performance stopping criterion for pareto-based evolutionary algorithms. In: Multi-Objective Programming and Goal Programming (2004)
16. Deb, K., Pratap, A., Agarwal, S.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6(8) (2002)
17. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C., Fonseca, V.: Performance assessment of multiobjective optimizers: An analysis and review. IEEE Trans. on Evolutionary Computation 8(2), 117-132 (2003)
18. Trautmann, H., Ligges, U., Mehnen, J., Preuss, M.: A convergence criterion for multiobjective evolutionary algorithms based on systematic statistical testing. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 825-836. Springer, Heidelberg (2008)
19. Knowles, J., Thiele, L., Zitzler, E.: A tutorial on the performance assessment of stochastic multiobjective optimizers. 214, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich (2005)
20. Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E.: PISA - A platform and programming language independent interface for search algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 494-508. Springer, Heidelberg (2003)
21. Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 2nd edn. Chapman and Hall, Boca Raton (2000)
22. Dudoit, S., van der Laan, M.: Multiple Testing Procedures with Applications to Genomics. Springer, Berlin (2008)
23. Stapleton, J.H.: Linear Statistical Models. Wiley Series in Probability and Statistics. Wiley, New York (1995)
24. Jensen, M.T.: Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms. IEEE Trans. on Evolutionary Computation 7(5), 503-515 (2003)
25. Beume, N., Rudolph, G.: Faster S-metric calculation by considering dominated hypervolume as Klee's measure problem. In: International Conference on Computational Intelligence (CI 2006) (2006)
26. Ihaka, R., Gentleman, R.: R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics 5, 299-314 (1996)
27. Fonseca, C.M., Fleming, P.J.: Multiobjective genetic algorithms made easy: Selection, sharing, and mating restriction. In: Genetic Algorithms in Engineering Systems: Innovations and Applications, pp. 42-52 (1995)
28. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8(2), 173-195 (2000)
29. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable multi-objective optimization test problems. In: Congress on Evolutionary Computation (CEC), vol. 1, pp. 825-830. IEEE Press, Piscataway (2002)
30. Deb, K.: Multi-objective Optimization using Evolutionary Algorithms. Wiley, Chichester (2001)
31. Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms - A comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292-301. Springer, Heidelberg (1998)
32. Hansen, M.P., Jaszkiewicz, A.: Evaluating the quality of approximations to the non-dominated set. Technical Report IMM-REP-1998-7 (1998)
33. Wagner, T., Michelitsch, T., Sacharow, A.: On the design of optimisers for surface reconstruction. In: Thierens, D., et al. (eds.) 9th Annual Genetic and Evolutionary Computation Conference (GECCO 2007), Proc., London, UK, pp. 2195-2202. ACM, New York (2007)